Towards a Precision Medicine Approach Based on Machine Learning for Tailoring Medical Treatment in Alkaptonuria

Abstract

ApreciseKUre is a multi-purpose digital platform facilitating data collection, integration and analysis for patients affected by Alkaptonuria (AKU), an ultra-rare autosomal recessive genetic disease. It includes genetic, biochemical, histopathological, clinical, therapeutic resources and quality of life scores that can be shared among registered researchers and clinicians in order to create a Precision Medicine Ecosystem (PME). The combination of machine learning application to analyse and re-interpret data available in the ApreciseKUre shows the potential direct benefits to achieve patient stratification and the consequent tailoring of care and treatments to a specific subgroup of patients. In this study, we have developed a tool able to investigate the most suitable treatment for AKU patients in accordance with their Quality of Life scores, which indicates changes in health status before/after the assumption of a specific class of drugs. This fact highlights the necessity of development of patient databases for rare diseases, like ApreciseKUre. We believe this is not limited to the study of AKU, but it represents a proof of principle study that could be applied to other rare diseases, allowing data management, analysis, and interpretation.

Effects of Nitisinone on Oxidative and Inflammatory Markers in Alkaptonuria: Results from SONIA1 and SONIA2 Studies

“…Furthermore, our results might support the use of SAA as a clinically relevant disease activity and severity marker in AKU. Overall, the datasets generated here along with those already stored in our AKU database [ 66 , 67 , 68 ] could be amenable to further analyses through artificial intelligence tools [ 66 , 67 , 68 , 69 , 70 , 71 ], helping us to better understand AKU and the analysis of genotype-phenotype correlations, possibly leading to a precision-medicine approach to AKU.…”

The Impact of Artificial Intelligence in the Odyssey of Rare Diseases

“…Thanks to these models, over 20 potential therapeutic targets were detected. In the last study [ 103 ], Spiga et al developed an RF model that performs a prediction of the QoL scores based on data deposited in ApreciseKUre. Predicted QoL scores were then correlated with the drugs taken by AKU patients, revealing that drugs typically used to treat AKU patients were effective in reducing pain, but some common drugs not related to specific AKU symptoms also showed a correlation with some QoL scores.…”

Preventive use of nitisinone in alkaptonuria

“…The currently employed Alkaptonuria Severity Score Index (AKUSSI) is an excellent summary of already established complaints and complications [ 11 ], but can not be used for early disease monitoring. Questionnaires like the Knee injury and Osteoarthritis Outcome Score (KOOS) for pain, symptoms, daily living, sport and quality of life, the Hip disability and Osteoarthritis Outcome Score (HOOS), and the Quality of Life for Osteoporosis questionnaire (QUALEFFO-41), as well as the Health Assessment Questionnaire Disability Index (HAQ-DI), measuring limitations in daily life, and the global pain visual analogue scale (hapVAS) have been used and can be used to prospectively monitor patient’s complaints and quality of life [ 12 ].…”